prereise/gather/solardata/nsrdb/demo/nsrdb_sam_demo.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# NSRDB and SAM for Solar Power Calculation\n",
    "---\n",
    "\n",
    "**NREL**: National Renewable Energy Laboratory\n",
    "\n",
    "**NSRDB**: National Solar Radiation Database  \n",
    "Information can be found at https://nsrdb.nrel.gov  \n",
    "API key Signup: https://developer.nrel.gov/signup/  \n",
    "API instructions: https://developer.nrel.gov/docs/solar/nsrdb/psm3-download/\n",
    "\n",
    "**SAM**: System Advisor Model  \n",
    "Information can be found at https://sam.nrel.gov/  \n",
    "SDK: https://sam.nrel.gov/sdk. The SDK needs to be downloaded.\n",
    "\n",
    "NSRDB is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations. NSRDB uses a physics-based modeling (PSM: Physical Solar Model) approach to provide solar radiation data for the entire United States in gridded segments (4x4 square kilometer) using geostationary satellites. Data can be accessed via an API.\n",
    "\n",
    "The [PVWatts v7](https://nrel-pysam.readthedocs.io/en/master/modules/Pvwattsv7.html) model is used to estimate the power output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from powersimdata.input.grid import Grid\n",
    "from prereise.gather.solardata.nsrdb import sam\n",
    "from prereise.gather.solardata.helpers import to_reise\n",
    "\n",
    "from getpass import getpass\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. Solar plants in Network"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Reading bus.csv\n",
      "Reading plant.csv\n",
      "Reading gencost.csv\n",
      "Reading branch.csv\n",
      "Reading dcline.csv\n",
      "Reading sub.csv\n",
      "Reading bus2sub.csv\n",
      "Reading zone.csv\n"
     ]
    }
   ],
   "source": [
    "grid = Grid(\"Texas\")\n",
    "solar_plant = grid.plant.groupby('type').get_group('solar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bus_id</th>\n",
       "      <th>Pg</th>\n",
       "      <th>Qg</th>\n",
       "      <th>Qmax</th>\n",
       "      <th>Qmin</th>\n",
       "      <th>Vg</th>\n",
       "      <th>mBase</th>\n",
       "      <th>status</th>\n",
       "      <th>Pmax</th>\n",
       "      <th>Pmin</th>\n",
       "      <th>...</th>\n",
       "      <th>type</th>\n",
       "      <th>interconnect</th>\n",
       "      <th>GenFuelCost</th>\n",
       "      <th>GenIOB</th>\n",
       "      <th>GenIOC</th>\n",
       "      <th>GenIOD</th>\n",
       "      <th>zone_id</th>\n",
       "      <th>zone_name</th>\n",
       "      <th>lat</th>\n",
       "      <th>lon</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plant_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>12878</th>\n",
       "      <td>3001011</td>\n",
       "      <td>9.99</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>12.00</td>\n",
       "      <td>1</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>solar</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "      <td>Far West</td>\n",
       "      <td>29.6000</td>\n",
       "      <td>-104.3000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12896</th>\n",
       "      <td>3001062</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>1.80</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>solar</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "      <td>Far West</td>\n",
       "      <td>32.2732</td>\n",
       "      <td>-101.4220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12897</th>\n",
       "      <td>3001063</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.01</td>\n",
       "      <td>276.00</td>\n",
       "      <td>0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>solar</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>301</td>\n",
       "      <td>Far West</td>\n",
       "      <td>32.2732</td>\n",
       "      <td>-101.4220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12984</th>\n",
       "      <td>3003120</td>\n",
       "      <td>29.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>47.52</td>\n",
       "      <td>1</td>\n",
       "      <td>39.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>solar</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>303</td>\n",
       "      <td>West</td>\n",
       "      <td>29.3289</td>\n",
       "      <td>-100.3840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13161</th>\n",
       "      <td>3006085</td>\n",
       "      <td>1.05</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.04</td>\n",
       "      <td>1.68</td>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>solar</td>\n",
       "      <td>Texas</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>306</td>\n",
       "      <td>South Central</td>\n",
       "      <td>30.5570</td>\n",
       "      <td>-97.6903</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 35 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           bus_id     Pg   Qg  Qmax  Qmin    Vg   mBase  status   Pmax  Pmin  \\\n",
       "plant_id                                                                       \n",
       "12878     3001011   9.99  0.0   0.0   0.0  1.00   12.00       1   10.0   0.0   \n",
       "12896     3001062   0.00  0.0   0.0   0.0  1.01    1.80       0    1.5   0.0   \n",
       "12897     3001063   0.00  0.0   0.0   0.0  1.01  276.00       0  230.0   0.0   \n",
       "12984     3003120  29.70  0.0   0.0   0.0  1.00   47.52       1   39.6   0.0   \n",
       "13161     3006085   1.05  0.0   0.0   0.0  1.04    1.68       1    1.4   0.0   \n",
       "\n",
       "          ...   type  interconnect  GenFuelCost  GenIOB  GenIOC  GenIOD  \\\n",
       "plant_id  ...                                                             \n",
       "12878     ...  solar         Texas          0.0     0.0     0.0       0   \n",
       "12896     ...  solar         Texas          0.0     0.0     0.0       0   \n",
       "12897     ...  solar         Texas          0.0     0.0     0.0       0   \n",
       "12984     ...  solar         Texas          0.0     0.0     0.0       0   \n",
       "13161     ...  solar         Texas          0.0     0.0     0.0       0   \n",
       "\n",
       "          zone_id      zone_name      lat       lon  \n",
       "plant_id                                             \n",
       "12878         301       Far West  29.6000 -104.3000  \n",
       "12896         301       Far West  32.2732 -101.4220  \n",
       "12897         301       Far West  32.2732 -101.4220  \n",
       "12984         303           West  29.3289 -100.3840  \n",
       "13161         306  South Central  30.5570  -97.6903  \n",
       "\n",
       "[5 rows x 35 columns]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "solar_plant.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 36 solar plants in the Texas interconnection.\n"
     ]
    }
   ],
   "source": [
    "print(\"There are %d solar plants in the Texas interconnection.\" % len(solar_plant))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. Downloading Data\n",
    "Visit https://developer.nrel.gov/signup/ to get your API key. Set the path to the SAM Simulation Core (SSC) library in `ssc_lib`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "email=daniel@breakthroughenergy.org\n",
      "api_key=········\n"
     ]
    }
   ],
   "source": [
    "email = input(prompt='email=')\n",
    "key = getpass(prompt='api_key=')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|██████████████████████████████████████████| 17/17 [00:51<00:00,  3.14s/it]\n"
     ]
    }
   ],
   "source": [
    "data = sam.retrieve_data_blended(email, key, grid=grid)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 07:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 08:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 09:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 10:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 11:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 12:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 13:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 14:00:00</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.059885</td>\n",
       "      <td>0.042145</td>\n",
       "      <td>0.042145</td>\n",
       "      <td>0.042145</td>\n",
       "      <td>0.042145</td>\n",
       "      <td>0.042145</td>\n",
       "      <td>0.042145</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 15:00:00</th>\n",
       "      <td>0.050717</td>\n",
       "      <td>0.054200</td>\n",
       "      <td>0.054200</td>\n",
       "      <td>0.113199</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>0.076352</td>\n",
       "      <td>...</td>\n",
       "      <td>0.213463</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.041453</td>\n",
       "      <td>0.016044</td>\n",
       "      <td>0.036233</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.236540</td>\n",
       "      <td>0.041453</td>\n",
       "      <td>0.042298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 16:00:00</th>\n",
       "      <td>0.110172</td>\n",
       "      <td>0.390702</td>\n",
       "      <td>0.390702</td>\n",
       "      <td>0.285293</td>\n",
       "      <td>0.399805</td>\n",
       "      <td>0.399805</td>\n",
       "      <td>0.399805</td>\n",
       "      <td>0.399805</td>\n",
       "      <td>0.399805</td>\n",
       "      <td>0.399805</td>\n",
       "      <td>...</td>\n",
       "      <td>0.408348</td>\n",
       "      <td>0.184071</td>\n",
       "      <td>0.184071</td>\n",
       "      <td>0.065924</td>\n",
       "      <td>0.233065</td>\n",
       "      <td>0.081087</td>\n",
       "      <td>0.184071</td>\n",
       "      <td>0.007065</td>\n",
       "      <td>0.065924</td>\n",
       "      <td>0.249503</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 17:00:00</th>\n",
       "      <td>0.206698</td>\n",
       "      <td>0.338267</td>\n",
       "      <td>0.338267</td>\n",
       "      <td>0.242969</td>\n",
       "      <td>0.631615</td>\n",
       "      <td>0.631615</td>\n",
       "      <td>0.631615</td>\n",
       "      <td>0.631615</td>\n",
       "      <td>0.631615</td>\n",
       "      <td>0.631615</td>\n",
       "      <td>...</td>\n",
       "      <td>0.031129</td>\n",
       "      <td>0.064183</td>\n",
       "      <td>0.064183</td>\n",
       "      <td>0.214012</td>\n",
       "      <td>0.171571</td>\n",
       "      <td>0.119463</td>\n",
       "      <td>0.064183</td>\n",
       "      <td>0.097522</td>\n",
       "      <td>0.214012</td>\n",
       "      <td>0.163943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 18:00:00</th>\n",
       "      <td>0.129504</td>\n",
       "      <td>0.401456</td>\n",
       "      <td>0.401456</td>\n",
       "      <td>0.141671</td>\n",
       "      <td>0.142196</td>\n",
       "      <td>0.142196</td>\n",
       "      <td>0.142196</td>\n",
       "      <td>0.142196</td>\n",
       "      <td>0.142196</td>\n",
       "      <td>0.142196</td>\n",
       "      <td>...</td>\n",
       "      <td>0.095960</td>\n",
       "      <td>0.296626</td>\n",
       "      <td>0.296626</td>\n",
       "      <td>0.192276</td>\n",
       "      <td>0.067980</td>\n",
       "      <td>0.151283</td>\n",
       "      <td>0.296626</td>\n",
       "      <td>0.245919</td>\n",
       "      <td>0.192276</td>\n",
       "      <td>0.209212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-01 19:00:00</th>\n",
       "      <td>0.076174</td>\n",
       "      <td>0.384640</td>\n",
       "      <td>0.384640</td>\n",
       "      <td>0.213146</td>\n",
       "      <td>0.126637</td>\n",
       "      <td>0.126637</td>\n",
       "      <td>0.126637</td>\n",
       "      <td>0.126637</td>\n",
       "      <td>0.126637</td>\n",
       "      <td>0.126637</td>\n",
       "      <td>...</td>\n",
       "      <td>0.040535</td>\n",
       "      <td>0.188210</td>\n",
       "      <td>0.188210</td>\n",
       "      <td>0.286320</td>\n",
       "      <td>0.101564</td>\n",
       "      <td>0.234526</td>\n",
       "      <td>0.188210</td>\n",
       "      <td>0.162571</td>\n",
       "      <td>0.286320</td>\n",
       "      <td>0.343501</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>20 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                        12878     12896     12897     12984     13161  \\\n",
       "2016-01-01 00:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 01:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 02:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 03:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 04:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 05:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 06:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 07:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 08:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 09:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 10:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 11:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 12:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 13:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 14:00:00  0.000000  0.000000  0.000000  0.059885  0.042145   \n",
       "2016-01-01 15:00:00  0.050717  0.054200  0.054200  0.113199  0.076352   \n",
       "2016-01-01 16:00:00  0.110172  0.390702  0.390702  0.285293  0.399805   \n",
       "2016-01-01 17:00:00  0.206698  0.338267  0.338267  0.242969  0.631615   \n",
       "2016-01-01 18:00:00  0.129504  0.401456  0.401456  0.141671  0.142196   \n",
       "2016-01-01 19:00:00  0.076174  0.384640  0.384640  0.213146  0.126637   \n",
       "\n",
       "                        13162     13163     13164     13165     13166  ...  \\\n",
       "2016-01-01 00:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 01:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 02:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 03:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 04:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 05:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 06:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 07:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 08:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 09:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 10:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 11:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 12:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 13:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  ...   \n",
       "2016-01-01 14:00:00  0.042145  0.042145  0.042145  0.042145  0.042145  ...   \n",
       "2016-01-01 15:00:00  0.076352  0.076352  0.076352  0.076352  0.076352  ...   \n",
       "2016-01-01 16:00:00  0.399805  0.399805  0.399805  0.399805  0.399805  ...   \n",
       "2016-01-01 17:00:00  0.631615  0.631615  0.631615  0.631615  0.631615  ...   \n",
       "2016-01-01 18:00:00  0.142196  0.142196  0.142196  0.142196  0.142196  ...   \n",
       "2016-01-01 19:00:00  0.126637  0.126637  0.126637  0.126637  0.126637  ...   \n",
       "\n",
       "                        13997     13998     13999     14000     14001  \\\n",
       "2016-01-01 00:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 01:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
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       "2016-01-01 03:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 04:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 05:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 06:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 07:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 08:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 09:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 10:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 11:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 12:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 13:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 14:00:00  0.000000  0.000000  0.000000  0.000000  0.000000   \n",
       "2016-01-01 15:00:00  0.213463  0.000000  0.000000  0.041453  0.016044   \n",
       "2016-01-01 16:00:00  0.408348  0.184071  0.184071  0.065924  0.233065   \n",
       "2016-01-01 17:00:00  0.031129  0.064183  0.064183  0.214012  0.171571   \n",
       "2016-01-01 18:00:00  0.095960  0.296626  0.296626  0.192276  0.067980   \n",
       "2016-01-01 19:00:00  0.040535  0.188210  0.188210  0.286320  0.101564   \n",
       "\n",
       "                        14002     14003     14004     14005     14006  \n",
       "2016-01-01 00:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 01:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 02:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 03:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 04:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 05:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 06:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 07:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 08:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 09:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 10:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 11:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 12:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 13:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 14:00:00  0.000000  0.000000  0.000000  0.000000  0.000000  \n",
       "2016-01-01 15:00:00  0.036233  0.000000  0.236540  0.041453  0.042298  \n",
       "2016-01-01 16:00:00  0.081087  0.184071  0.007065  0.065924  0.249503  \n",
       "2016-01-01 17:00:00  0.119463  0.064183  0.097522  0.214012  0.163943  \n",
       "2016-01-01 18:00:00  0.151283  0.296626  0.245919  0.192276  0.209212  \n",
       "2016-01-01 19:00:00  0.234526  0.188210  0.162571  0.286320  0.343501  \n",
       "\n",
       "[20 rows x 36 columns]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(n=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>12878</th>\n",
       "      <th>12896</th>\n",
       "      <th>12897</th>\n",
       "      <th>12984</th>\n",
       "      <th>13161</th>\n",
       "      <th>13162</th>\n",
       "      <th>13163</th>\n",
       "      <th>13164</th>\n",
       "      <th>13165</th>\n",
       "      <th>13166</th>\n",
       "      <th>...</th>\n",
       "      <th>13997</th>\n",
       "      <th>13998</th>\n",
       "      <th>13999</th>\n",
       "      <th>14000</th>\n",
       "      <th>14001</th>\n",
       "      <th>14002</th>\n",
       "      <th>14003</th>\n",
       "      <th>14004</th>\n",
       "      <th>14005</th>\n",
       "      <th>14006</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "      <td>8784.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.319473</td>\n",
       "      <td>0.305552</td>\n",
       "      <td>0.305552</td>\n",
       "      <td>0.280769</td>\n",
       "      <td>0.275564</td>\n",
       "      <td>0.275564</td>\n",
       "      <td>0.275564</td>\n",
       "      <td>0.275564</td>\n",
       "      <td>0.275564</td>\n",
       "      <td>0.275564</td>\n",
       "      <td>...</td>\n",
       "      <td>0.310299</td>\n",
       "      <td>0.302906</td>\n",
       "      <td>0.302906</td>\n",
       "      <td>0.313706</td>\n",
       "      <td>0.313175</td>\n",
       "      <td>0.315599</td>\n",
       "      <td>0.302906</td>\n",
       "      <td>0.317617</td>\n",
       "      <td>0.313706</td>\n",
       "      <td>0.297031</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.392525</td>\n",
       "      <td>0.384133</td>\n",
       "      <td>0.384133</td>\n",
       "      <td>0.365295</td>\n",
       "      <td>0.363075</td>\n",
       "      <td>0.363075</td>\n",
       "      <td>0.363075</td>\n",
       "      <td>0.363075</td>\n",
       "      <td>0.363075</td>\n",
       "      <td>0.363075</td>\n",
       "      <td>...</td>\n",
       "      <td>0.389951</td>\n",
       "      <td>0.385331</td>\n",
       "      <td>0.385331</td>\n",
       "      <td>0.391299</td>\n",
       "      <td>0.390747</td>\n",
       "      <td>0.393994</td>\n",
       "      <td>0.385331</td>\n",
       "      <td>0.395716</td>\n",
       "      <td>0.391299</td>\n",
       "      <td>0.379438</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.770420</td>\n",
       "      <td>0.726192</td>\n",
       "      <td>0.726192</td>\n",
       "      <td>0.660864</td>\n",
       "      <td>0.628115</td>\n",
       "      <td>0.628115</td>\n",
       "      <td>0.628115</td>\n",
       "      <td>0.628115</td>\n",
       "      <td>0.628115</td>\n",
       "      <td>0.628115</td>\n",
       "      <td>...</td>\n",
       "      <td>0.748457</td>\n",
       "      <td>0.728051</td>\n",
       "      <td>0.728051</td>\n",
       "      <td>0.745061</td>\n",
       "      <td>0.763090</td>\n",
       "      <td>0.766505</td>\n",
       "      <td>0.728051</td>\n",
       "      <td>0.763064</td>\n",
       "      <td>0.745061</td>\n",
       "      <td>0.705764</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>...</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>8 rows × 36 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             12878        12896        12897        12984        13161  \\\n",
       "count  8784.000000  8784.000000  8784.000000  8784.000000  8784.000000   \n",
       "mean      0.319473     0.305552     0.305552     0.280769     0.275564   \n",
       "std       0.392525     0.384133     0.384133     0.365295     0.363075   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.770420     0.726192     0.726192     0.660864     0.628115   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "             13162        13163        13164        13165        13166  ...  \\\n",
       "count  8784.000000  8784.000000  8784.000000  8784.000000  8784.000000  ...   \n",
       "mean      0.275564     0.275564     0.275564     0.275564     0.275564  ...   \n",
       "std       0.363075     0.363075     0.363075     0.363075     0.363075  ...   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  ...   \n",
       "75%       0.628115     0.628115     0.628115     0.628115     0.628115  ...   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  ...   \n",
       "\n",
       "             13997        13998        13999        14000        14001  \\\n",
       "count  8784.000000  8784.000000  8784.000000  8784.000000  8784.000000   \n",
       "mean      0.310299     0.302906     0.302906     0.313706     0.313175   \n",
       "std       0.389951     0.385331     0.385331     0.391299     0.390747   \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000   \n",
       "75%       0.748457     0.728051     0.728051     0.745061     0.763090   \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000   \n",
       "\n",
       "             14002        14003        14004        14005        14006  \n",
       "count  8784.000000  8784.000000  8784.000000  8784.000000  8784.000000  \n",
       "mean      0.315599     0.302906     0.317617     0.313706     0.297031  \n",
       "std       0.393994     0.385331     0.395716     0.391299     0.379438  \n",
       "min       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "25%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "50%       0.000000     0.000000     0.000000     0.000000     0.000000  \n",
       "75%       0.766505     0.728051     0.763064     0.745061     0.705764  \n",
       "max       1.000000     1.000000     1.000000     1.000000     1.000000  \n",
       "\n",
       "[8 rows x 36 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. Plot Data\n",
    "One solar plant is selected. The power output (in MW) is then plotted for the entire year for 72-h in June."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capacity of the plant: 230.0 MW\n"
     ]
    }
   ],
   "source": [
    "plant_id = 12897\n",
    "print(\"Capacity of the plant: %.1f MW\" % solar_plant.loc[plant_id].Pmax)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "data_oneplant = data[plant_id] * solar_plant.loc[plant_id, \"Pmax\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(16,8))\n",
    "ax = fig.gca()\n",
    "data_oneplant.plot(ax=ax, legend=False, fontsize=15, lw=2)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Electric Energy (MWh)', fontsize=20)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = pyplot.figure(figsize=(16,8))\n",
    "ax = fig.gca()\n",
    "data_oneplant[4000:4072].plot(ax=ax, legend=False, fontsize=15, lw=2)\n",
    "ax.set_xlabel('')\n",
    "ax.set_ylabel('Electric Energy (MWh)', fontsize=20)\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Capacity Factor: 0.306\n"
     ]
    }
   ],
   "source": [
    "print(\"Capacity Factor: %.3f\" % (data_oneplant.sum() / (366 * 24 * solar_plant.loc[plant_id].Pmax)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}